Observability Metrics Logs and Traces for Modern Apps

Observability helps teams understand how modern apps behave in production. By collecting data from metrics, logs, and traces, you can spot issues early and reduce downtime. These three pillars work together to reveal not just what happened, but why.

Metrics give numbers over time. They help you see trends and set alerts. Common metrics include latency, error rate, and request rate, plus signals of saturation like queue depth or CPU usage. With clear dashboards, teams spot problems before users notice.

  • Latency (p95, p99)
  • Error rate
  • Throughput
  • Saturation signals such as queue depth and CPU

Logs provide context and events. Use structured logs with fields like request_id, user_id, and service_name. Centralized storage makes it easy to search for incidents and to connect events to a specific action.

  • Structured logging
  • Correlation IDs
  • Centralized log storage

Traces show how a request moves through services. They reveal timing, dependencies, and slow paths. Use sampling to manage data volume, and propagate a trace context across services so logs and metrics can be linked to the same user action.

  • Distributed tracing across services
  • Span relationships and timing
  • Trace context propagation

To get started, instrument early and choose a standard toolset. OpenTelemetry fits many stacks. Collect core metrics, enable structured logging, and turn on tracing with sensible sampling. Tag data with consistent fields like service, region, and version.

A simple workflow helps teams stay effective: define what to measure, instrument your code, store data in a centralized system, and review dashboards after incidents. When trouble hits, look at metrics to gauge scope, read logs for sequence, and open traces to find root causes.

In the end, observability is a shared practice. Metrics, logs, and traces are parts of one story about how apps behave under real load.

Key Takeaways

  • Use the three pillars to diagnose issues faster.
  • Standardize instrumentation and correlation IDs to connect data.
  • Build practical dashboards and alerts to catch issues early.